1. Technical Field
The present disclosure relates to diffusion tensor imaging, and more particularly to directional statistics visualization for diffusion tensor imaging (DTI).
2. Description of Related Art
Diffusion tensor image scans comprise at least six gradient directions, sufficient to determine a diffusion tensor in, for example, a brain scan. From the diffusion tensor, diffusion anisotropy measures such as the Fractional Anisotropy (FA) can be determined. Moreover, the principal direction of the diffusion tensor can be used to infer white-matter connectivity of the brain and model it as a tract.
A visualization strategy for such a tract is to render a diffusion ellipsoid at a subset of data points. Since a three-dimensional field of ellipsoids would occlude each other, this visualization is typically done for two-dimensional slices of data. Additionally, only ellipsoids on a sparse grid can be rendered in order for each ellipsoid to be discerned. This type of visualization can become visually cluttered, obscuring the information intended to be conveyed.
Previous work for visualization has included a texture based approach, generating an image by blurring a source image in the direction of the vector field at each point. Line integral convolution (LIC) is one technique for implementing this visualization. Another approach with substantially similar visual results uses the solution of a partial differential equation (PDE).
Rendering techniques used for texture-based DT-MRI visualization include planar surface rendering and volume rendering. In planar surface rendering, the image is determined for a planar slice of the DT-MRI data, and displayed as a textured quadrilateral. The volume rendering technique includes determining the texture at each point in the volume and rendering the field by ray-casting, or some similar technique. These techniques can be computationally expensive.
Therefore, a need exists for directional statistics visualization for diffusion tensor imaging.